A new decision tree construction using the cloud transform and rough sets

Research output: Contribution to report/book/conference proceedings › In-proceedings paper

Standard

A new decision tree construction using the cloud transform and rough sets. / Song, Jing; Li, Tianrui; Ruan, Da; Turcanu, Catrinel (Peer reviewer).

Rough Sets and Knowledge Technology. Vol. 1 Heidelberg, Germany, 2008. p. 524-531 (Lecture Notes in Artificial Intelligence (LNAI) (5009); No. ISSN 0302-9743).

Research output: Contribution to report/book/conference proceedings › In-proceedings paper

Harvard

Song, J, Li, T, Ruan, D & Turcanu, C 2008, A new decision tree construction using the cloud transform and rough sets. in Rough Sets and Knowledge Technology. vol. 1, Lecture Notes in Artificial Intelligence (LNAI) (5009), no. ISSN 0302-9743, Heidelberg, Germany, pp. 524-531, Third International Conference, RSKT 2008, Chengdu, China, 2008-05-17.

APA

Song, J., Li, T., Ruan, D., & Turcanu, C. (2008). A new decision tree construction using the cloud transform and rough sets. In Rough Sets and Knowledge Technology (Vol. 1, pp. 524-531). (Lecture Notes in Artificial Intelligence (LNAI) (5009); No. ISSN 0302-9743). Heidelberg, Germany.

Vancouver

Song J, Li T, Ruan D, Turcanu C. A new decision tree construction using the cloud transform and rough sets. In Rough Sets and Knowledge Technology. Vol. 1. Heidelberg, Germany. 2008. p. 524-531. (Lecture Notes in Artificial Intelligence (LNAI) (5009); ISSN 0302-9743).

Author

Song, Jing ; Li, Tianrui ; Ruan, Da ; Turcanu, Catrinel. / A new decision tree construction using the cloud transform and rough sets. Rough Sets and Knowledge Technology. Vol. 1 Heidelberg, Germany, 2008. pp. 524-531 (Lecture Notes in Artificial Intelligence (LNAI) (5009); ISSN 0302-9743).

Bibtex - Download

@inproceedings{c37b13f21b494221b88813dd1daf9b23,
title = "A new decision tree construction using the cloud transform and rough sets",
abstract = "Many present methods for dealing with the continuous data and missing values in information systems for constructing decision tree do not perform well in practical applications. In this paper, a new algorithm, Decision Tree Construction based on the Cloud Transform and Rough Set Theory under Characteristic Relation (DTCCRSCR), is proposed for mining classification knowledge from the data set. The cloud transform is applied to discretize continuous data and the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as the current splitting node. Experimental results show the decision trees constructed by DTCCRSCR tend to have a simpler structure, much higher classification accuracy and more understandable rules than C5.0 in most cases.",
keywords = "Rough sets, Cloud transform, Decision trees, Weighted mean roughness, Characteristic relation",
author = "Jing Song and Tianrui Li and Da Ruan and Catrinel Turcanu",
note = "Score = 1",
year = "2008",
month = "5",
language = "English",
isbn = "978-3-540-79720-3",
volume = "1",
series = "Lecture Notes in Artificial Intelligence (LNAI) (5009)",
number = "ISSN 0302-9743",
pages = "524--531",
booktitle = "Rough Sets and Knowledge Technology",

}

RIS - Download

TY - GEN

T1 - A new decision tree construction using the cloud transform and rough sets

AU - Song, Jing

AU - Li, Tianrui

AU - Ruan, Da

A2 - Turcanu, Catrinel

N1 - Score = 1

PY - 2008/5

Y1 - 2008/5

N2 - Many present methods for dealing with the continuous data and missing values in information systems for constructing decision tree do not perform well in practical applications. In this paper, a new algorithm, Decision Tree Construction based on the Cloud Transform and Rough Set Theory under Characteristic Relation (DTCCRSCR), is proposed for mining classification knowledge from the data set. The cloud transform is applied to discretize continuous data and the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as the current splitting node. Experimental results show the decision trees constructed by DTCCRSCR tend to have a simpler structure, much higher classification accuracy and more understandable rules than C5.0 in most cases.

AB - Many present methods for dealing with the continuous data and missing values in information systems for constructing decision tree do not perform well in practical applications. In this paper, a new algorithm, Decision Tree Construction based on the Cloud Transform and Rough Set Theory under Characteristic Relation (DTCCRSCR), is proposed for mining classification knowledge from the data set. The cloud transform is applied to discretize continuous data and the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as the current splitting node. Experimental results show the decision trees constructed by DTCCRSCR tend to have a simpler structure, much higher classification accuracy and more understandable rules than C5.0 in most cases.

KW - Rough sets

KW - Cloud transform

KW - Decision trees

KW - Weighted mean roughness

KW - Characteristic relation

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_88631

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_88631_2

UR - http://knowledgecentre.sckcen.be/so2/bibref/4979

M3 - In-proceedings paper

SN - 978-3-540-79720-3

VL - 1

T3 - Lecture Notes in Artificial Intelligence (LNAI) (5009)

SP - 524

EP - 531

BT - Rough Sets and Knowledge Technology

CY - Heidelberg, Germany

ER -

ID: 105990